Agents and Data Mining Interaction: 4th International by Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky,

By Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky, Jiming Liu, Gerhard Weiss, Philip S. Yu (eds.)

This e-book constitutes the completely refereed post-conference court cases of the 4th foreign Workshop on brokers and information Mining interplay, ADMI 2009, held in Budapest, Hungary in may perhaps 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth foreign Joint convention on self reliant brokers and Multiagent Systems.

The 12 revised papers and a pair of invited talks provided have been conscientiously reviewed and chosen from a number of submissions. geared up in topical sections on agent-driven facts mining, information mining pushed brokers, and agent mining purposes, the papers express the exploiting of agent-driven information mining and the resolving of severe facts mining difficulties in concept and perform; find out how to increase facts mining-driven brokers, and the way information mining can increase agent intelligence in examine and useful purposes. topics which are additionally addressed are exploring the combination of brokers and information mining in the direction of a super-intelligent details processing and structures, and settling on demanding situations and instructions for destiny examine at the synergy among brokers and knowledge mining.

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Extra info for Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers

Example text

To calculate the logical error, it is necessary to define logically correct and logically incorrect decisions. As applied to the task of forecasting product life cycle phase transition period, logically correct and logically incorrect decisions could be described as follows: 1. Assume that discrete time series d has a duration equal to ld , but the value of the key parameter - the period of product life cycle phase transition, is p = pd , where pd > ld . This statement means that a real time of transition between the phases of the product life cycle has not come yet.

The Data Management Agent performs several tasks of managing data. It has a link to the database that contains the product demand data and regularly is updated. The Data Management Agent handles the preprocessing of the data - data normalization, exclusion of obvious outliers and data transformation to defined format before sending it to the Data Mining Agent. Such preprocessing allows 38 S. Parshutin and A. Borisov Fig. 1. Structure of the system to lessen the impact of noisiness and dominance of data [14,16].

Luo, and C. Zhang – Network intelligence, refers to both web intelligence and broad-based network intelligence, – Organizational intelligence, refers to organizational goals, structures, rules, and dynamics, and – Social intelligence, refers to both human social intelligence and agent/animat-based social intelligence. We have discussed their definition, aims, aspects and techniques for involving them into agents, data mining, and agent mining. In our other works, preliminary examples and case studies have been presented to illustrate these concepts.

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